Capturing and using massive volumes of data has enabled financial services firms to transform not only how they work, but how they interact with their customers. It has led to a revolution in everybody’s mindset, and advanced analytics have been at the centre of capitalising on new data-driven business opportunities.
Research by analytics powerhouse SAS in 2018 shows that the greatest benefit of real-time customer analytics is overwhelmingly an improvement in customer experiences. Additionally, more than half their survey respondents also note that they’re reaping more streamlined operations and efficiencies in sales and marketing, faster decision making, and improved collaboration between marketing, sales, services and operations. 40% of their respondents say real-time customer analytics also can lead to increased innovation, as well as the introduction of new business models, products and services.
Advanced analytics is the collection and scrutiny of data using technology that goes above and beyond traditional tools to achieve pattern matching, mining data from disparate sources, behavioural analysis, sentiment analysis, predictive and prescriptive analytics, use of machine learning, natural language processing and forecasting. The outcome of all this analysis help financial firms reduce risk and boost revenue as internal processes become better optimised, fraudulent transactions are preempted, risky and profitable customers are identified quickly, and growth areas are highlighted earlier.
Machine learning enables systems to rapidly analyse vast amounts of data which no longer includes traditional financial data, but also customer interactions with your company via text message, voice calls, social media and touch screens. This insight allows customer engagement to be monitored, logged, analysed and to produce recommendations to improve satisfaction. Accuracy and granularity allows segmentation to be precise and thorough, meaning customers are offered products and services that are perfectly bespoke to their needs.
The insights generated help financial institutions to reduce risk and raise revenue by optimising internal processes, pre-empting fraudulent transactions, identifying risky and profitable customers and highlighting growth areas. Customers benefit because the time taken to produce lending decisions is reduced, and firms become more quickly able to assist them in forecasting savings and investments over the long term. Since each touchpoint along the customer journey produces data, the service becomes more personalised and their financial requirements become better understood.
Another benefit to the customer is the use of robo-advisors which help them make investment decisions based on peer-to-peer comparisons or customer-specific portfolio advice. A robo-advisor doesn’t need human input, basing all decisions on algorithms developed on the customer’s risk profile.
Having to cope with a potentially unlimited supply of data means data visualisation is crucial to successful advanced analytics, especially in identifying patterns and trends. This helps both the adviser make more accurate decisions, and the customer view the state of their portfolio through intuitive and automated personal finance dashboards. The dashboard can identify investment opportunities based on the customer’s risk profile and available funds, or use past data to understand trends and encourage better habits.
Data-driven analytics has become fundamental to the future growth of financial institutions and with the technology still in its early days, it is something all asset managers will have to deal with.
The real challenge is not just in understanding how to best serve customers with this growing technology, but where to find the greatest value and how to present it clearly, in a concise and practical way.
Discover more about how technology is transforming the financial services landscape by reading our latest insights.
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